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This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95. [ABSTRACT FROM AUTHOR]

Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight "conditional-probability-based" bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a "classification-free" modification, in which factor classifications are avoided and the result is dominated by a single parameter, "bin width". Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

Due to the prevalence of the order production mode, multi-variety, small-batch manufacturing enterprises frequently delay deliveries to downstream customers. To date, most existing studies on delayed delivery risk have focused on the response to the risk after it occurs, ignoring how the risk arises. For multi-variety, low-volume production companies, any part of the production process could lead to the ultimate risk of delayed delivery, and the risk is transmissible. Therefore, the path of risk transmission needs to be identified to effectively control the risk of late delivery at key production stages. In this paper, from the perspective of risk transmission, a recognition method based on association rules and the Bayesian network was proposed to identify the risk conduction path. This method firstly determined the strong association rules among the risk factors based on historical data stored in the ERP system and determined the Bayesian network topology structures of the risk transmission path by combining the business process and expert experience. Secondly, the prior and conditional probabilities of each node were determined using data statistics, and the risk transmission path of delayed delivery was identified using the forward and backward reasoning of the Bayesian network. Finally, this paper provided a case study to verify the method, and the following conclusions were obtained: (1) the delay in delivery to downstream customers is mainly due to the delayed delivery of upstream suppliers and the sudden change in customer demand, and (2) the adjustment of enterprise production plans is the key node of the delayed delivery risk transmission path. Through the research in this paper, production companies can identify the target of risk management more scientifically and mitigate the risk through the adjustment of key links. [ABSTRACT FROM AUTHOR]

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing. [ABSTRACT FROM AUTHOR]

The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information. The optimal update is obtained by minimizing either the Hellinger distance or the quadratic Bregman divergence. The results obtained by the two methods differ. Updates with information about conditional probabilities are considered as well. [ABSTRACT FROM AUTHOR]